Generative Artificial Intelligence in Healthcare: Automation Bias, Deskilling, and Cognitive Implications - A Systematic Review.
Authors
Affiliations (1)
Affiliations (1)
- Department Management Information Systems, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia.
Abstract
Generative artificial intelligence (GenAI) is rapidly transforming eHealthcare, promising substantial gains in diagnostic accuracy, workflow efficiency, and decision support, yet raising concerns about automation bias and clinician deskilling that may erode core diagnostic expertise and professional judgment over time. This systematic review investigates the dual impact of GenAI in eHealthcare, focusing on how it enhances clinical efficiency and decision support while potentially diminishing clinicians' diagnostic expertise and professional judgment. A systematic search was conducted across PubMed, Scopus, Web of Science, and Google Scholar. A total of 11,269 records were identified, and 29 studies met inclusion criteria following PRISMA 2020 guidelines. Studies were synthesized using a narrative approach due to heterogeneity in design, clinical domains, and AI system maturity. Across the included studies, recurrent themes included automation bias (reported in 10 studies), concerns regarding clinician deskilling (9 studies), and impacts on diagnostic reasoning (9 studies). Evidence was predominantly observational, experimental simulation-based, or conceptual in nature. Findings reveal that GenAI significantly improves diagnostic accuracy, workflow efficiency, and decision quality across radiology, telehealth, and education domains. However, overreliance introduces risks of automation bias, cognitive deskilling, and loss of interpretive autonomy. The review identifies key mitigation strategies, including human-in-the-loop frameworks, explainable AI (XAI), ethical governance, and continuous clinician reskilling. Theoretically, the results redefine the clinician-AI relationship as a dynamic cognitive partnership, while practically they emphasize responsible integration through education and regulation. Sustainable adoption of GenAI demands balanced implementation-leveraging its analytical capabilities without compromising human judgment, clinical reasoning, or professional accountability.